Prediction of atrazine degradation in soil based on XGBoost model.

We established the optimal model by using the automatic machine learning method to predict the degradation efficiency of herbicide atrazine in soil, which could be used to assess the residual risk of atrazine in soil. We collected 494 pairs of data from 49 published articles, and selected seven factors as input features, including soil pH, organic matter content, saturated hydraulic conductivity, soil moisture, initial concentration of atrazine, incubation time, and inoculation dose. Using the first-order reaction rate constant of atrazine in soil as the output feature, we established six models to predict the degradation efficiency of atrazine in soil, and conducted comprehensive analysis of model performance through linear regression and related evaluation indicators. The results showed that the XGBoost model had the best performance in predicting the first-order reaction rate constant (k). Based on the prediction model, the feature importance ranking of each factor was in an order of soil moisture > incubation time > pH > organic matter > initial concentration of atrazine > saturated hydraulic conductivity > inoculation dose. We used SHAP to explain the potential relationship between each feature and the degradation ability of atrazine in soil, as well as the relative contribution of each feature. Results of SHAP showed that time had a negative contribution and saturated hydraulic conductivity had a positive contribution. High values of soil moisture, initial concentration of atrazine, pH, inoculation dose and organic matter content were generally distributed on both sides of SHAP=0, indicating their complex contributions to the degradation of atrazine in soil. The XGBoost model method combined with the SHAP method had high accuracy in predicting the performance and interpretability of the k model. By using machine learning method to fully explore the value of historical experimental data and predict the degradation efficiency of atrazine using environmental parameters, it is of great significance to set the threshold for atrazine application, reduce the residual and diffusion risks of atrazine in soil, and ensure the safety of soil environment.

Spectroscopic techniques for quantitative characterization of Cu (II) and Hg (II) complexation by dissolved organic matter from lake sediment in arid and semi-arid region.

Dissolved organic matter (DOM) was extracted from six sediment samples in arid and semi-arid region, which was characterized by fluorescence excitation-emission matrices (EEMs). The results showed that four fluorescent peak, fulvic-like (peak A), humic-like (peak C) and two tryptophan-like (peaks B and D), were identified in lake sediment DOM. Fluorescence quenching titration showed that peaks B and D were quenched gradually by adding additional Cu (II) and Hg (II), whereas humic-like substances had no systematic trend of the change of fluorescence intensity. Increasing fluorescence intensity value of humic-like substances can also be found. The modified Stern-Volmer model was used to calculate conditional stability constants (logK) and the percent of fluorophores (f %) which participate in the complexation between DOM and Cu (II), and Hg (II). The results showed that DOM-Cu (II) and DOM-Hg (II) complexes had higher logK values of 4.21-5.23 and the logK values of DOM-Cu (II) are much larger than the corresponding values for Hg (II). Peak B showed relatively low logK and high f % values than those of peak D. Different pollution sources which are mainly obtained from the upstream industrial wastewater, domestic sewage and return water of farmland irrigation tend to affect the stability constants and complexing capacities of Cu (II) and Hg (II).